MS-DS Master of Data science Machine Learning 3

Question 1

What does the VC dimension measure in statistical learning theory?

Accepted Answer

The capacity or complexity of a hypothesis class, defined by the largest set it can shatter

Answer

The VC dimension is the size of the largest set of points that a hypothesis class can classify correctly in all possible ways, quantifying model capacity.

Question 2

In principal component analysis (PCA), what do the principal components represent?

Accepted Answer

Orthogonal directions of maximum variance in the data

Answer

PCA finds orthogonal axes (principal components) that capture the directions of maximum variance, sorted from highest to lowest explained variance.

Question 3

Which cross-validation strategy is most appropriate when the dataset has a temporal ordering?

Accepted Answer

Time-series split (walk-forward validation)

Answer

Time-series split trains on past observations and validates on future ones, preserving temporal order and preventing data leakage from future to past.

Question 4

In the Expectation-Maximization (EM) algorithm for Gaussian Mixture Models, what happens in the E-step?

Accepted Answer

Soft responsibilities (posterior probabilities of cluster membership) are computed for each point

Answer

The E-step computes the expected cluster membership (responsibility) of each data point given the current model parameters using Bayes' theorem.

Question 5

What does SMOTE (Synthetic Minority Oversampling TEchnique) do to address class imbalance?

Accepted Answer

Creates synthetic minority samples by interpolating between existing minority instances

Answer

SMOTE generates new synthetic minority samples by selecting a minority point and interpolating along the line segment to one of its k nearest minority neighbors.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Machine Learning 3